Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models

Matsuki, Akari; Tanaka, Gouhei

doi:10.1103/physreve.100.022302

Cited by 19 publications

(12 citation statements)

References 56 publications

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“…First, we may be able to exploit our observation that the node2vec mobility with small a and b suppresses the spread of infections to inform intervention methods. For example, the node2vec random walk may change the efficiency of the intervention into epidemic dynamics on networks of subpopulations with which one selectively lessens the infection rate within subpopulations having large degrees [51][52][53]. Under the simple random walk, containment of the epidemic has also been examined in combination with adaptive dynamics, with which individuals cancel their travel in response to their infection status [28,54] or adjust their movement based on the information about the safety level measured by the number of susceptible individuals in various subpopulations [54][55][56][57].…”

Section: Discussionmentioning

confidence: 99%

Epidemic dynamics on metapopulation networks with node2vec mobility

Meng,

Masuda

2021

Preprint

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Metapopulation models have been a powerful tool for both theorizing and simulating epidemic dynamics. In a metapopulation model, one considers a network composed of subpopulations and their pairwise connections, and individuals are assumed to migrate from one subpopulation to another obeying a given mobility rule. While how different mobility rules affect epidemic dynamics in metapopulation models has been much studied, there have been relatively few efforts on systematic comparison of the effects of simple (i.e., unbiased) random walks and more complex mobility rules. Here we study a susceptible-infected-susceptible (SIS) dynamics in a metapopulation model, in which individuals obey a parametric second-order random-walk mobility rule called the node2vec. We map the second-order mobility rule of the node2vec to a first-order random walk in a network whose each node is a directed edge connecting a pair of subpopulations and then derive the epidemic threshold. For various networks, we find that the epidemic threshold is large (therefore, epidemic spreading tends to be suppressed) when the individuals infrequently backtrack or infrequently visit the common neighbors of the currently visited and the last visited subpopulations than when the individuals obey the simple random walk. The amount of change in the epidemic threshold induced by the node2vec mobility is in general not as large as, but is sometimes comparable with, the one induced by the change in the overall rate at which individuals diffuse from one subpopulation to another.

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Section: Discussionmentioning

confidence: 99%

Epidemic dynamics on metapopulation networks with node2vec mobility

Meng,

Masuda

2021

Preprint

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“…We introduce 10 infected individuals to one node chosen at random, build a tree data structure with this node at the root, and run a stochastic metapopulation SIR simulation on the network. At each time interval of duration dt, the following steps are taken: [27]. Each individual who moves between nodes may be Susceptible, Infected, or Recovered.…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

“…A random selection of individuals moves from node i to each neighboring node j , as a binomial distribution with probability [ 27 ]. Each individual who moves between nodes may be Susceptible, Infected, or Recovered.…”

Section: Methodsmentioning

confidence: 99%

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Lieberthal

Gardner

2021

PLoS Comput Biol

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Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.

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“…In recent years, the rise of the research in complex networks provides a new perspective for the study of epidemic spreading [8]. It is necessary to establish the corresponding network model for the study of different influencing factors of epidemics, e.g., the contact between people [9,10], the immunity of particular individuals [11][12][13], the migration behavior [14][15][16], etc.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Information Propagation on Individual Migration Routes and Epidemic Spread Over Metapopulation Networks

Wang

Gou

Han

2021

Preprint

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Information propagation driven by the epidemic may cause the awareness of individuals to change their behavior, thus preventing themselves from being infected. For example, the aware individuals migrate away from areas with severe infection. In this paper, we study the coupling transmission of epidemic and information in metapopulation networks, and mainly explore how the change of individual migration behavior affects the epidemic spreading. Combined with the transition probability tree of individual states, we use Markov chain approach for theoretical analysis and derive the epidemic threshold. Through numerous Monte Carlo simulation, we verify the accuracy of Markov equations for the prediction of epidemic sprading. The results show that the role of information transmission in suppressing the epidemic in terms of the epidemic threshold and the infection scale is very limited. Further increase of information transmission rate beyond its critical value will no longer affect the epidemic. The initial population distribution is a fundamental factor in the epidemic dynamics, and in the case of heterogeneous distribution, an appropriate movement of individuals can delay the epidemic spread with a smaller threshold. In addition, topological homogeneity of individual migration route is beneficial for the epidemic control. This study analyzes the interaction between epidemic and information on the metapopulation network model, which can provide guidance for epidemic intervention in reality.

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Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models

Cited by 19 publications

References 56 publications

Epidemic dynamics on metapopulation networks with node2vec mobility

Epidemic dynamics on metapopulation networks with node2vec mobility

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Impacts of Information Propagation on Individual Migration Routes and Epidemic Spread Over Metapopulation Networks

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